HW5 - Make Data Move
05/10/2020
Explore the recent global developments with R
Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
Get the necessary packages
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
Look at the data
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
unique(gapminder$year)## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007head(gapminder)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
We see an interesting spread with an outlier to the right. Answer the following questions, please:
Q1. Why does it make sense to have a log10 scale on x axis?
If not making it log10, the outlier would affect the visualisation so much, that it is almost impossible to see how the rest of the countries are doing compared to each other in gdpPercap. When using the log10, this outlier is not affecting the rest of the visualisation as much.
Q2. What country is the richest in 1952 (far right on x axis)?
gapminder %>%
filter(year == 1952, gdpPercap > 100000)## # A tibble: 1 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.Showing that Kuwait was the richest country in 1952
You can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Q3. Can you differentiate the continents by color and fix the axis labels?
In order to make the plot show, I had to remove the legend, which contains a lot of country-names, thus not making room for the actual plot to show.
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, colour = country, xlab = "test")) +
geom_point() +
theme(legend.position = "none") + # removing the legend in order to display the plot (and not only the legend)
scale_x_log10() +
labs(y= "Life Expectancy", x = "GDP per capita") # Putting prettier labels on the axes
Q4. What are the five richest countries in the world in 2007?
Since I have removed the labels in order to see the plot, I figure this out by using filter(), arrange() and head() instead.
gapminder %>%
filter(year == 2007) %>%
arrange(desc(gdpPercap)) %>%
head(5)## # A tibble: 5 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.Here are the five richest countries in the world in 2007: Norway, Kuwait, Singapore, United States & Ireland.
Make it move!
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.
Option 1: Animate using transition_states()
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
Option 2 Animate using transition_time()
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively]
# Adding the title in the first version, that is using transition_states()
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
labs(title="Year {closest_state}")
anim + transition_states(year,
transition_length = 1,
state_length = 1)
# Adding the title in the second version, that is using transition_time()
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
labs(title = "Year {frame_time}") +
transition_time(year)
anim2
Q6 Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]
# Fixing labels and units in the first version, that is using transition_states()
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10(labels = scales::comma) +
labs(title="Year {closest_state}", x = "GDP per capita", y = "Life Expectancy")
anim + transition_states(year,
transition_length = 1,
state_length = 1)
# Fixing labels and units in the second version, that is using transition_time()
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10(labels = scales::comma) +
labs(title = "Year {frame_time}", x = "GDP per capita", y = "Life Expectancy") +
transition_time(year)
anim2
Q7 Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]
My question: How has the population growth in Oceania been developing?
gapminder %>%
filter(continent == "Oceania") %>%
ggplot(aes(year, pop, colour = country)) +
geom_point() +
scale_y_continuous(labels = scales::comma) +
geom_line() +
labs(title = "Population growth in Oceania", x = "Year", y = "Population")
This plot shows how the populations in Oceania (Australia and New Zealand) has developed with different paces. Australia, which also had a bigger population as a starting point (for this dataset) has a higher increase in their population compared to New Zealand, parlicularly when looking at it in absolute numbers as we do here.